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cospancospan
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Added image processing directory
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‎image_processor/.custom_harris_test.py.swp‎

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‎image_processor/.opencv_harris_test.py.swp‎

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‎image_processor/checkerboard2.png‎

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‎image_processor/chessboard.jpg‎

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‎image_processor/chessboard.png‎

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‎image_processor/chessboard_skew.jpg‎

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‎image_processor/custom_harris_test.py‎

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#! /usr/bin/env python
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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from image_processor import *
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print (TCOLORS.PURPLE + "Custom Corner Detector" + TCOLORS.NORMAL)
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COLOR_OUT = True
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#FILENAME = 'chessboard_skew.jpg'
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#FILENAME = 'checkerboard2.png'
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FILENAME = "lena1.png"
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#Generate the Gaussian Envelope
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#Constants
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#SIGMA=1.6
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BLOCK_SIZE = 3
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#BLOCK_SIZE = 5
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#BLOCK_SIZE = 7
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K_VALUE = 0.04
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#K_VALUE = 0.15
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THRESHOLD = 50000
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#SIGMA = float(BLOCK_SIZE) / 3.0
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SIGMA = float(BLOCK_SIZE) / 6.0
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img = cv2.imread(FILENAME)
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gray = rgb2gray(img)
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cstm_out = cv2.imread(FILENAME)
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ocv_out = cv2.imread(FILENAME)
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if not COLOR_OUT:
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cstm_out = rgb2gray(cstm_out)
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ocv_out = rgb2gray(cstm_out)
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print "Image Width: %d Image Height: %d" % (len(gray[0]), len(gray))
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print "Sigma: %f" % SIGMA
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print "Envelope Size: %d" % BLOCK_SIZE
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print "Generating Gaussian Array...",
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start_time = time.time()
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#gaussian_array = gen_deviation_array(sigma = SIGMA, length = BLOCK_SIZE)
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gaussian_array = gen_2d_deviation_array(sigma = SIGMA, length = BLOCK_SIZE)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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print "Gausian Distribution:\n%s" % str(gaussian_array)
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print "Creating the X and Y image derivatives...",
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start_time = time.time()
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image_x, image_y = gen_image_derivatives(gray)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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print "Generate <Ix^2*W(u,v)>, <IxIy*W(u,v)>, <Iy^2*W(u,v)>...",
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start_time = time.time()
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a, bc, d = generate_matrix_values(image_x, image_y, gaussian_array)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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print "Find the R Values:...",
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start_time = time.time()
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corners, det, trc = generate_mc_debug(a, bc, d, K_VALUE, THRESHOLD)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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print "Display original image, gray image, DX Image, DY Image and Gaussian Array"
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print "Apply to gray image"
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height = len(gray)
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width = len(gray[0])
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A = np.ndarray(shape=(len(a), len(a[0])))
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BC = np.ndarray(shape=(len(a), len(a[0])))
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D = np.ndarray(shape=(len(a), len(a[0])))
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for y in range(height):
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for x in range(width):
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A[y,x] = int(a[y,x])
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BC[y,x] = int(bc[y,x])
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D[y,x] = int(d[y,x])
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ocv_gray = np.float32(gray)
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ocv = cv2.cornerHarris(ocv_gray,
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BLOCK_SIZE,
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3,
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K_VALUE)
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ocv = cv2.dilate(ocv,None)
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if COLOR_OUT:
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corners = cv2.dilate(corners,None)
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cstm_out[corners == 255]=[0, 255, 0]
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ocv_out[ocv > (0.01 * ocv.max())]=[0, 255, 0]
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else:
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cstm_out[corners == 255]=[255]
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ocv_out[ocv > (0.01 * ocv.max())]=[255]
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fig = plt.figure()
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a=fig.add_subplot(5,3,1)
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plt.title("Original")
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plt.imshow(gray, cmap='Greys_r')
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plt.axis('off')
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a=fig.add_subplot(5,3,2)
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plt.title("Gaussian")
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plt.imshow(gaussian_array, cmap='Greys_r')
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plt.axis('off')
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a=fig.add_subplot(5,3,4)
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plt.title("X Derivative")
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plt.imshow(image_x, cmap='Greys_r')
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plt.axis('off')
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a=fig.add_subplot(5,3,5)
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plt.title("Y Derivative")
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plt.imshow(image_y, cmap='Greys_r')
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plt.axis('off')
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f=fig.add_subplot(5,3,7)
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plt.title("A = <Ix^2 * w(u, v)>")
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plt.imshow(A, cmap="Greys_r")
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plt.axis('off')
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f=fig.add_subplot(5,3,8)
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plt.title("B and C = <IxIy * w(u, v)>")
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plt.imshow(BC, cmap="Greys_r")
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plt.axis('off')
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f=fig.add_subplot(5,3,9)
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plt.title("D = <Iy^2 * w(u,v)")
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plt.imshow(D, cmap="Greys_r")
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plt.axis('off')
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f=fig.add_subplot(5,3,10)
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plt.title("Determinate(M)")
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plt.imshow(det, cmap="Greys_r")
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plt.axis('off')
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f=fig.add_subplot(5,3,11)
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plt.title("k * trace(M) ^ 2")
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plt.imshow(trc, cmap="Greys_r")
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plt.axis('off')
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if COLOR_OUT:
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f=fig.add_subplot(5,3,13)
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plt.title("Custom HCD Result")
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plt.imshow(cstm_out)
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plt.axis('off')
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f=fig.add_subplot(5,3,14)
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plt.title("Openc CV HCD Result")
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plt.imshow(ocv_out)
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plt.axis('off')
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else:
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f=fig.add_subplot(5,3,13)
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plt.title("Custom HCD Result")
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#plt.imshow(corners, cmap="Greys_r")
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plt.imshow(cstm_out, cmap="Greys_r")
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plt.axis('off')
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f=fig.add_subplot(5,3,14)
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plt.title("Openc CV HCD Result")
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plt.imshow(ocv_out, cmap="Greys_r")
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plt.axis('off')
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plt.show()
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‎image_processor/gaussian_image_test.py‎

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#! /usr/bin/env python
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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from image_processor import *
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print (TCOLORS.PURPLE + "Unit Test: Applying a Gaussian Blure on an image" + TCOLORS.NORMAL)
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SIGMA = 1
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ENVELOPE_SIZE = 5
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img = cv2.imread(FILENAME)
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gray = rgb2gray(img)
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width = len(gray)
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height = len(gray[0])
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print "Image Width: %d Image Height: %d" % (len(gray), len(gray[0]))
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print "Sigma: %d" % SIGMA
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print "Envelope Size: %d" % ENVELOPE_SIZE
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print "Generating Gaussian Envelope...",
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start_time = time.time()
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gaussian_envelope = gen_deviation_array(SIGMA, ENVELOPE_SIZE)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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print "Gaussian Envelope: %s" % str(gaussian_envelope)
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print "Using Open CV to apply gaussian blur...",
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start_time = time.time()
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blur = cv2.GaussianBlur(gray,(SIGMA,SIGMA),0)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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print "Using custom algorithm to apply gaussian blur...",
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start_time = time.time()
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gaussian_blure = two_d_gaussian_blure(gray, gaussian_envelope)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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fig = plt.figure()
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a=fig.add_subplot(1,3,1)
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plt.title("Original")
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plt.imshow(gray, cmap='Greys_r')
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a=fig.add_subplot(1,3,2)
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plt.title("Opencv Gaussian Blur")
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plt.imshow(blur, cmap='Greys_r')
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a=fig.add_subplot(1,3,3)
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plt.title("Gaussian Blur")
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plt.imshow(gaussian_blure, cmap='Greys_r')
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plt.show()
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‎image_processor/gaussian_test.py‎

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#! /usr/bin/env python
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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from image_processor import *
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print (TCOLORS.PURPLE + "Unit Test: Generate a standard guassian Array and digital representation of a guassian array" + TCOLORS.NORMAL)
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#Constants
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gaussian_sigma=1.2
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gaussian_bitrange=18 #This is the number of bits that will be used to multiply values in the FPGA
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gaussian_width = 5
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print (TCOLORS.RED + "Gaussian Array" + TCOLORS.NORMAL)
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print ("\tSigma: %f" % gaussian_sigma)
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print ("\tBitrange: %d (Max Value) %d" % (gaussian_bitrange, ((2 ** gaussian_bitrange) - 1)))
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print ("\tArray Length: %d" % gaussian_width)
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gaussian_array = gen_deviation_array(sigma = gaussian_sigma, length = gaussian_width)
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digital_array = convert_gaussian_to_digital_array(gaussian_array, gaussian_bitrange)
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fig = plt.figure()
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a=fig.add_subplot(1,2,1)
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plt.bar(range( 0, len(gaussian_array)), gaussian_array)
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plt.title("Normalized Gaussian Envelope")
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plt.ylabel("Weight")
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plt.xlabel("Envelope Position")
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plt.xticks(range( 0, len(gaussian_array)), range(0 * int(gaussian_width / 2), len(gaussian_array)))
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a=fig.add_subplot(1,2,2)
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plt.bar(range( 0, len(digital_array)), digital_array)
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plt.title("Mapped to %d bits" % gaussian_bitrange)
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plt.ylabel("Weight")
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plt.xlabel("Envelope Position")
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plt.xticks(range( 0, len(digital_array)), range(-1 * int(gaussian_width / 2), len(digital_array)))
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plt.ylim([0, (2 ** gaussian_bitrange) - 1])
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plt.show()

‎image_processor/image_derivative_test.py‎

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#! /usr/bin/env python
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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from image_processor import *
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print (TCOLORS.PURPLE + "Unit Test: Finding the derivatives of an image" + TCOLORS.NORMAL)
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img = cv2.imread(FILENAME)
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image = rgb2gray(img)
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print "Generating X and Y derivative of image...",
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start_time = time.time()
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image_x, image_y = gen_image_derivatives(image)
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elapsed_time = time.time() - start_time
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print "Done: Elapsed Time: %.3f" % elapsed_time
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fig = plt.figure()
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a=fig.add_subplot(1,3,1)
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plt.title("Original")
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plt.imshow(image, cmap='Greys_r')
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a=fig.add_subplot(1,3,2)
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plt.title("X Derivative")
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plt.imshow(image_x, cmap='Greys_r')
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a=fig.add_subplot(1,3,3)
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plt.title("Y Derivative")
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plt.imshow(image_y, cmap='Greys_r')
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plt.show()

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